System

Abstract

An object of a system according to an embodiment is to provide efficient and user-friendly questionnaire experience.SOLUTION: A system includes a question generation part, a chat interface part, a data collection part, and a point giving part. The question generation unit learns the user's personal information and past questionnaire responses and generates personalized questions. The chat interface unit presents the question generated by the question generation unit to the user. The data collection unit analyzes the user's answer collected by the chat interface unit. The point awarding unit awards points to the user on the basis of the result analyzed by the data collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that surveys are time-consuming and it is difficult to provide a user-friendly experience.

[0005] The system according to the embodiment aims to provide an efficient and user-friendly survey experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, a chat interface unit, a data collection unit, and a point assignment unit. The question generation unit learns personal information and past survey responses of a user and generates personalized questions. The chat interface unit presents the questions generated by the question generation unit to the user. The data collection unit analyzes the user's responses collected by the chat interface unit. The point assignment unit assigns points to the user based on the results of the analysis by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an efficient and user-friendly survey experience. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A survey service according to an embodiment of the present invention is a system that learns from a user's personal information and past survey responses, uses a generation AI to generate personalized questions, collects responses through simple chat-style interactions, and awards points. This allows the survey service to provide interactions tailored to the user's interests, enabling companies and researchers to efficiently collect market data and opinions.

[0029] A survey service according to an embodiment includes a question generation unit, a chat interface unit, a data collection unit, and a point assignment unit. The question generation unit learns a user's personal information and past survey responses and generates personalized questions. For example, if a user has previously answered a survey about "travel," the generation AI generates a question such as "Where do you want to go on your next trip?" The generation AI can also generate questions based on the user's interests and concerns. The chat interface unit presents the questions generated by the question generation unit to the user. For example, a user can easily participate in the survey at any time using a smartphone or a PC. The chat interface unit is designed so that the survey progresses simply by the user entering a simple answer such as "yes" or "no." The data collection unit analyzes the user's responses collected by the chat interface unit. For example, the generation AI analyzes the collected data and provides useful information for companies and researchers. The data collection unit can also compile survey results and provide reports on the popularity of specific products and consumer opinions. The point assignment unit assigns points to the user based on the results analyzed by the data collection unit. For example, if a user answers 10 surveys, 100 points are awarded, and the user can use the points to obtain a gift card. Furthermore, the point awarding unit awards points for each survey answer to maintain the user's motivation. This allows the survey service according to the embodiment to provide interactions tailored to the user's interests, enabling companies and researchers to efficiently collect market data and opinions. For example, by answering questions tailored to their interests, users can maintain their interest in surveys, and companies and researchers can efficiently collect and analyze data.

[0030] The question generation unit can analyze a user's social media activity and generate questions based on their latest interests. For example, the question generation unit uses a generation AI to analyze a user's social media posts and generate questions based on their latest interests and trends. For example, if a user has recently posted about "movies," the question generation unit can generate a question such as "What movie did you see recently?" The question generation unit can also adjust the content and format of questions based on the user's social media activity. For example, if a user shows interest in a particular topic, the question generation unit can generate questions related to that topic. This allows the system to continue to attract the user's attention by generating questions based on the user's latest interests.

[0031] The question generation unit can analyze a user's past answer patterns and generate questions that take into account the consistency and changes in answers. For example, the question generation unit uses a generation AI to analyze a user's past answer data and generate questions that maintain the consistency of answers. For example, for a user who has consistently shown interest in questions about "travel" in the past, detailed questions about travel are generated. The question generation unit can also generate questions that take into account changes in the user's answer patterns. For example, if a user has recently started to show an interest in "sports," questions related to sports are generated. This makes it possible to continue to attract the user's interest by generating questions based on the user's past answer patterns.

[0032] The question generation unit generates questions in different fields based on the user's hobbies and interests, thereby drawing out new interests of the user. For example, the question generation unit uses a generation AI to analyze the user's hobbies and interests and generate questions in different fields based on the results. For example, for a user who likes music, the question generation unit generates questions related to movies and art. The question generation unit can also combine questions from different fields to draw out the user's interests. For example, if a user is interested in "sports" and "travel," the question generation unit generates travel questions related to sporting events. This makes it possible to draw out new interests of the user by generating questions in different fields based on the user's hobbies and interests.

[0033] The question generation unit can utilize the user's geographical information to generate region-specific questions. For example, the question generation unit uses a generation AI to analyze the user's geographical information and generate questions specific to that region. For example, if the user lives in Tokyo, the question generation unit can generate questions such as, "What are the recommended tourist spots in Tokyo?" The question generation unit can also generate questions based on region-specific events and culture. For example, it generates questions about events held in a specific region. This allows the user to continue to attract region-specific interest by generating questions based on the user's geographical information.

[0034] The chat interface unit can analyze the user's response speed and adjust the difficulty of the question if the response is slow. For example, the chat interface unit uses a generation AI to analyze the user's response speed in real time and lower the difficulty of the question if the response is slow. For example, it changes a complex question to a simpler question. The chat interface unit can also adjust the content and format of the question depending on the user's response speed. For example, if the user is responding quickly, it generates a detailed question. This adjusts the difficulty of the question depending on the user's response speed, thereby reducing the user's stress.

[0035] The chat interface unit can analyze the user's past response history and generate follow-up questions to maintain consistency in the responses. For example, the chat interface unit uses a generation AI to analyze the user's past response history and generate follow-up questions to maintain consistency. For example, if a user has previously answered questions about "travel," the next question generated will be about "travel plans." The chat interface unit can also adjust the content and format of questions based on the user's response history. For example, if a user has consistently shown interest in a particular topic, follow-up questions related to that topic can be generated. This makes it possible to maintain the user's interest by generating follow-up questions based on the user's past response history.

[0036] The chat interface unit can utilize the user's device information to present questions in the optimal interface. For example, the chat interface unit uses a generation AI to analyze the user's device information and present questions in the optimal interface. For example, it presents short questions on a smartphone and detailed questions on a PC. The chat interface unit can also adjust the interface depending on the type of device and screen size. For example, it provides an interface suitable for touch operation on a tablet. This improves user convenience by presenting questions in the optimal interface based on the user's device information.

[0037] The chat interface unit can find commonalities with other users based on the user's response history and generate questions based on common topics. For example, the chat interface unit uses a generation AI to analyze the user's response history and find commonalities with other users. For example, it generates questions based on common topics between users with the same hobby. The chat interface unit can also adjust the content and format of questions based on common topics. For example, it generates questions related to common interests. This makes it possible to continue to attract the user's attention by generating questions based on common topics based on the user's response history.

[0038] The data collection unit can analyze the user's response history and perform data analysis that takes into account consistency and changes in responses. For example, the data collection unit uses a generation AI to analyze the user's past response history and prioritize analysis of consistent data. For example, it places emphasis on data from users who have consistent opinions on the same topic. The data collection unit can also perform data analysis that takes into account changes in the user's response patterns. For example, if a user has recently started to show interest in "sports," it will prioritize analysis of data related to sports. This allows for data analysis based on the user's response history to obtain more accurate market data and opinions.

[0039] The data collection unit can analyze the user's social media activity and perform data analysis based on the latest trends. For example, the data collection unit uses a generative AI to analyze the user's social media posts and perform data analysis based on the latest trends. For example, market trends can be identified based on the content of recent posts. The data collection unit can also adjust the content and method of data analysis based on the user's social media activity. For example, if a user shows interest in a particular topic, data related to that topic can be analyzed preferentially. This makes it possible to identify the latest market trends by performing data analysis based on the user's social media activity.

[0040] The data collection unit can integrate data from different industries to provide cross-industry insights. For example, the data collection unit uses generative AI to collect, integrate, and analyze data from different industries. For example, it can provide insights that combine data from the technology field and the consumer market. The data collection unit can also analyze similarities and differences between different industries. For example, it can compare data from the IT industry and the medical industry to identify common trends. This makes it possible to provide cross-industry insights by integrating data from different industries.

[0041] The data collection unit can utilize the user's geographical information to perform region-specific data analysis. For example, the data collection unit uses the generation AI to analyze the user's geographical information and perform region-specific data analysis. For example, analyzing consumer behavior in a specific region. The data collection unit can also perform data analysis based on region-specific events and culture. For example, analyzing data related to events held in a specific region. In this way, region-specific market data and opinions can be obtained by performing data analysis based on the user's geographical information.

[0042] The point assigning unit can analyze the user's answer history and assign points based on the consistency and quality of the answers. For example, the point assigning unit uses a generation AI to analyze the user's past answer history and assign bonus points to consistent answers. For example, additional points are assigned to users who have consistent opinions on the same topic. The point assigning unit can also adjust the point assignment criteria based on the quality of the user's answers. For example, bonus points are assigned to users who provide detailed answers. In this way, by assigning points based on the user's answer history, it is possible to maintain the user's motivation.

[0043] The point assigning unit can analyze a user's social media activity and assign points based on the activity. For example, the point assigning unit uses a generation AI to analyze a user's social media posts and assign points based on the activity. For example, the point assigning unit assigns bonus points to posts that receive many likes or shares. The point assigning unit can also adjust the criteria for assigning points based on the user's social media activity. For example, if a user shows interest in a particular topic, points are assigned for activities related to that topic. In this way, by assigning points based on the user's social media activity, it is possible to maintain the user's motivation.

[0044] The point assigning unit can assign bonus points to answers to specific topics based on the user's hobbies and interests. For example, the point assigning unit uses a generation AI to analyze the user's hobbies and interests and assigns bonus points to answers to specific topics based on the analysis. For example, a user who likes music can be assigned bonus points when answering a question about music. The point assigning unit can also adjust the criteria for assigning points to answers to specific topics in order to attract the user's interest. For example, bonus points can be assigned to topics in which the user has recently become interested. In this way, the user's motivation can be maintained by assigning points based on the user's hobbies and interests.

[0045] The point assigning unit can utilize the user's geographical information to assign bonus points to responses to region-specific questionnaires. For example, the point assigning unit uses a generation AI to analyze the user's geographical information and assign bonus points to responses to questionnaires specific to that region. For example, if the user lives in Tokyo, bonus points are assigned when the user answers a questionnaire about Tokyo. The point assigning unit can also adjust the point assignment criteria for questionnaires based on region-specific events or culture. For example, bonus points are assigned when the user answers a questionnaire about events held in a specific region. This allows the user's motivation to be maintained by assigning points based on the user's geographical information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The survey service can also analyze the user's health data and generate questions based on their health status. For example, it can analyze data from the user's fitness tracker to generate questions about their exercise habits. It can also generate questions about their sleep quality based on the user's sleep data. This allows the survey service to generate questions based on the user's health status and keep the user engaged.

[0048] The survey service can also analyze a user's purchasing history and generate questions based on their purchasing behavior. For example, it can generate questions to ask for feedback on a product the user recently purchased. It can also analyze a user's purchasing patterns and generate questions about the next product they might purchase. This allows the service to generate questions based on the user's purchasing behavior and keep the user engaged.

[0049] The question generation unit can analyze a user's past answer patterns and generate questions that take into account the consistency and changes in answers. For example, the generation AI analyzes a user's past answer data and generates questions that maintain the consistency of answers. For example, for a user who has consistently shown interest in questions about "travel" in the past, detailed questions about travel are generated. The question generation unit can also generate questions that take into account changes in the user's answer patterns. For example, if a user has recently started to show an interest in "sports," questions related to sports are generated. This makes it possible to continue to attract the user's interest by generating questions based on the user's past answer patterns.

[0050] The survey service can also analyze a user's learning history and generate questions based on the learning content. For example, if a user is taking an online course, the survey service can generate questions to check the user's understanding of the course. It can also generate questions about what the user should learn next based on the user's learning progress. This allows the survey service to keep the user engaged by generating questions based on the user's learning history.

[0051] The question generation unit can utilize the user's geographical information to generate questions specific to that region. For example, the generation AI analyzes the user's geographical information and generates questions specific to that region. For example, if the user lives in Tokyo, it can generate a question such as, "What are the recommended tourist spots in Tokyo?" The question generation unit can also generate questions based on local events and culture. For example, it generates questions about events held in a specific region. This allows the generation of questions based on the user's geographical information to continue to attract local interest.

[0052] The chat interface unit can analyze the user's response speed and adjust the difficulty of the question if the response is slow. For example, the generation AI can analyze the user's response speed in real time and lower the difficulty of the question if the response is slow. For example, it can change a complex question to a simpler one. The chat interface unit can also adjust the content and format of the question according to the user's response speed. For example, if the user is responding quickly, it can generate a more detailed question. This can reduce the user's stress by adjusting the difficulty of the question according to the user's response speed.

[0053] The chat interface unit can analyze the user's past response history and generate follow-up questions to maintain consistency in the responses. For example, the generation AI analyzes the user's past response history and generates follow-up questions to maintain consistency. For example, if a user has previously answered questions about "travel," the next question generated will be about "travel plans." The chat interface unit can also adjust the content and format of questions based on the user's response history. For example, if a user has consistently shown interest in a particular topic, follow-up questions related to that topic can be generated. This makes it possible to maintain the user's interest by generating follow-up questions based on the user's past response history.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The question generation unit learns the user's personal information and past survey responses and generates personalized questions. For example, if the user has previously answered a survey about "travel," the generation AI will generate a question such as "Where do you want to go on your next trip?" The generation AI can also generate questions based on the user's interests and concerns. Step 2: The chat interface unit presents the questions generated by the question generator to the user. For example, the user can easily participate in the survey at any time using a smartphone or PC. The chat interface unit is also designed so that the survey can proceed by the user simply entering simple answers such as "yes" or "no." Step 3: The data collection unit analyzes the user responses collected by the chat interface unit. For example, the generative AI analyzes the collected data and provides useful information for companies and researchers. The data collection unit can also compile survey results and provide reports on the popularity of specific products and consumer opinions. Step 4: The point allocating unit allocates points to the user based on the results of the analysis by the data collecting unit. For example, if a user answers 10 surveys, 100 points are allocated, and the user can use these points to obtain a gift card. The point allocating unit also allocates points for each survey answer to maintain the user's motivation.

[0056] (Example 2) A survey service according to an embodiment of the present invention is a system that learns from a user's personal information and past survey responses, uses a generation AI to generate personalized questions, collects responses through simple chat-style interactions, and awards points. This allows the survey service to provide interactions tailored to the user's interests, enabling companies and researchers to efficiently collect market data and opinions.

[0057] A survey service according to an embodiment includes a question generation unit, a chat interface unit, a data collection unit, and a point assignment unit. The question generation unit learns a user's personal information and past survey responses and generates personalized questions. For example, if a user has previously answered a survey about "travel," the generation AI generates a question such as "Where do you want to go on your next trip?" The generation AI can also generate questions based on the user's interests and concerns. The chat interface unit presents the questions generated by the question generation unit to the user. For example, a user can easily participate in the survey at any time using a smartphone or a PC. The chat interface unit is designed so that the survey progresses simply by the user entering a simple answer such as "yes" or "no." The data collection unit analyzes the user's responses collected by the chat interface unit. For example, the generation AI analyzes the collected data and provides useful information for companies and researchers. The data collection unit can also compile survey results and provide reports on the popularity of specific products and consumer opinions. The point assignment unit assigns points to the user based on the results analyzed by the data collection unit. For example, if a user answers 10 surveys, 100 points are awarded, and the user can use the points to obtain a gift card. Furthermore, the point awarding unit awards points for each survey answer to maintain the user's motivation. This allows the survey service according to the embodiment to provide interactions tailored to the user's interests, enabling companies and researchers to efficiently collect market data and opinions. For example, by answering questions tailored to their interests, users can maintain their interest in surveys, and companies and researchers can efficiently collect and analyze data.

[0058] The question generation unit can estimate the user's emotional state in real time and generate questions that correspond to that emotional state. For example, the question generation unit uses a generation AI to analyze the user's facial expressions and vocal tone to estimate the emotional state in real time. For example, if the user is feeling stressed, the question generation unit generates questions that will help them relax. The question generation unit can also adjust the content and format of questions according to the user's emotional state. For example, if the user is expressing joy, the question generation unit generates positive questions. This makes it possible to keep the user engaged by generating questions that correspond to the user's emotional state.

[0059] The question generation unit can analyze a user's social media activity and generate questions based on their latest interests. For example, the question generation unit uses a generation AI to analyze a user's social media posts and generate questions based on their latest interests and trends. For example, if a user has recently posted about "movies," the question generation unit can generate a question such as "What movie did you see recently?" The question generation unit can also adjust the content and format of questions based on the user's social media activity. For example, if a user shows interest in a particular topic, the question generation unit can generate questions related to that topic. This allows the system to continue to attract the user's attention by generating questions based on the user's latest interests.

[0060] The question generation unit can analyze a user's past answer patterns and generate questions that take into account the consistency and changes in answers. For example, the question generation unit uses a generation AI to analyze a user's past answer data and generate questions that maintain the consistency of answers. For example, for a user who has consistently shown interest in questions about "travel" in the past, detailed questions about travel are generated. The question generation unit can also generate questions that take into account changes in the user's answer patterns. For example, if a user has recently started to show an interest in "sports," questions related to sports are generated. This makes it possible to continue to attract the user's interest by generating questions based on the user's past answer patterns.

[0061] The question generation unit generates questions in different fields based on the user's hobbies and interests, thereby drawing out new interests of the user. For example, the question generation unit uses a generation AI to analyze the user's hobbies and interests and generate questions in different fields based on the results. For example, for a user who likes music, the question generation unit generates questions related to movies and art. The question generation unit can also combine questions from different fields to draw out the user's interests. For example, if a user is interested in "sports" and "travel," the question generation unit generates travel questions related to sporting events. This makes it possible to draw out new interests of the user by generating questions in different fields based on the user's hobbies and interests.

[0062] The question generation unit can utilize the user's geographical information to generate region-specific questions. For example, the question generation unit uses a generation AI to analyze the user's geographical information and generate questions specific to that region. For example, if the user lives in Tokyo, the question generation unit can generate questions such as, "What are the recommended tourist spots in Tokyo?" The question generation unit can also generate questions based on region-specific events and culture. For example, it generates questions about events held in a specific region. This allows the user to continue to attract region-specific interest by generating questions based on the user's geographical information.

[0063] The question generation unit uses the emotion estimation function to generate questions based on the topics that interest the user most, thereby maintaining the user's interest. For example, the question generation unit uses the emotion estimation function to identify the topics that interest the user most and generate questions based on those topics. For example, if the user shows a strong interest in "music," the question generation unit generates a question such as "What is your favorite song that you've listened to recently?" The question generation unit can also adjust the content and format of the question based on the user's emotional state. For example, if the user is relaxed, the question generation unit generates a detailed question. This allows the user's interest to be maintained by generating questions based on the topics that interest the user most.

[0064] The chat interface unit can estimate the user's emotional state in real time and generate a response that corresponds to the emotional state. For example, the generation AI in the chat interface unit analyzes the user's facial expressions and voice tone to estimate the emotional state in real time. For example, if the user is tired, it prioritizes simple questions. The chat interface unit can also adjust the content and format of the response according to the user's emotional state. For example, if the user is expressing joy, it generates a positive response. This makes it possible to maintain the user's interest by generating a response that corresponds to the user's emotional state.

[0065] The chat interface unit can analyze the user's response speed and adjust the difficulty of the question if the response is slow. For example, the chat interface unit uses a generation AI to analyze the user's response speed in real time and lower the difficulty of the question if the response is slow. For example, it changes a complex question to a simpler question. The chat interface unit can also adjust the content and format of the question depending on the user's response speed. For example, if the user is responding quickly, it generates a detailed question. This adjusts the difficulty of the question depending on the user's response speed, thereby reducing the user's stress.

[0066] The chat interface unit can analyze the user's past response history and generate follow-up questions to maintain consistency in the responses. For example, the chat interface unit uses a generation AI to analyze the user's past response history and generate follow-up questions to maintain consistency. For example, if a user has previously answered questions about "travel," the next question generated will be about "travel plans." The chat interface unit can also adjust the content and format of questions based on the user's response history. For example, if a user has consistently shown interest in a particular topic, follow-up questions related to that topic can be generated. This makes it possible to maintain the user's interest by generating follow-up questions based on the user's past response history.

[0067] The chat interface unit can utilize the user's device information to present questions in the optimal interface. For example, the chat interface unit uses a generation AI to analyze the user's device information and present questions in the optimal interface. For example, it presents short questions on a smartphone and detailed questions on a PC. The chat interface unit can also adjust the interface depending on the type of device and screen size. For example, it provides an interface suitable for touch operation on a tablet. This improves user convenience by presenting questions in the optimal interface based on the user's device information.

[0068] The chat interface unit can find commonalities with other users based on the user's response history and generate questions based on common topics. For example, the chat interface unit uses a generation AI to analyze the user's response history and find commonalities with other users. For example, it generates questions based on common topics between users with the same hobby. The chat interface unit can also adjust the content and format of questions based on common topics. For example, it generates questions related to common interests. This makes it possible to continue to attract the user's attention by generating questions based on common topics based on the user's response history.

[0069] The chat interface unit uses the emotion estimation function to present questions at the timing when the user is most relaxed, thereby improving the quality of the answers. For example, the chat interface unit uses the emotion estimation function to identify the timing when the user is most relaxed and presents questions at that timing. For example, when the user is relaxed, a question such as "What are your recent hobbies?" is generated. The chat interface unit can also adjust the content and format of the questions based on the user's emotional state. For example, if the user is feeling stressed, it prioritizes simple questions. This makes it possible to present questions at the timing when the user is most relaxed, thereby improving the quality of the answers.

[0070] The data collection unit can estimate the user's emotional state in real time and perform data analysis based on the emotional state. For example, the data collection unit uses a generation AI to estimate the user's emotional state in real time and prioritize collecting opinions from users with positive emotions. For example, if a user expresses joy, that opinion is given more weight. The data collection unit can also adjust the content and method of data analysis based on the user's emotional state. For example, if a user is feeling stressed, data analysis aimed at reducing stress is performed. In this way, more accurate market data and opinions can be obtained by performing data analysis based on the user's emotional state.

[0071] The data collection unit can analyze the user's response history and perform data analysis that takes into account consistency and changes in responses. For example, the data collection unit uses a generation AI to analyze the user's past response history and prioritize analysis of consistent data. For example, it places emphasis on data from users who have consistent opinions on the same topic. The data collection unit can also perform data analysis that takes into account changes in the user's response patterns. For example, if a user has recently started to show interest in "sports," it will prioritize analysis of data related to sports. This allows for data analysis based on the user's response history to obtain more accurate market data and opinions.

[0072] The data collection unit can analyze the user's social media activity and perform data analysis based on the latest trends. For example, the data collection unit uses a generative AI to analyze the user's social media posts and perform data analysis based on the latest trends. For example, market trends can be identified based on the content of recent posts. The data collection unit can also adjust the content and method of data analysis based on the user's social media activity. For example, if a user shows interest in a particular topic, data related to that topic can be analyzed preferentially. This makes it possible to identify the latest market trends by performing data analysis based on the user's social media activity.

[0073] The data collection unit can integrate data from different industries to provide cross-industry insights. For example, the data collection unit uses generative AI to collect, integrate, and analyze data from different industries. For example, it can provide insights that combine data from the technology field and the consumer market. The data collection unit can also analyze similarities and differences between different industries. For example, it can compare data from the IT industry and the medical industry to identify common trends. This makes it possible to provide cross-industry insights by integrating data from different industries.

[0074] The data collection unit can utilize the user's geographical information to perform region-specific data analysis. For example, the data collection unit uses the generation AI to analyze the user's geographical information and perform region-specific data analysis. For example, analyzing consumer behavior in a specific region. The data collection unit can also perform data analysis based on region-specific events and culture. For example, analyzing data related to events held in a specific region. In this way, region-specific market data and opinions can be obtained by performing data analysis based on the user's geographical information.

[0075] The data collection unit can use the emotion estimation function to perform data analysis based on the user's emotions and identify data that is likely to be emotionally relatable. The data collection unit, for example, uses the emotion estimation function to perform data analysis based on the user's emotions. For example, it prioritizes analysis of opinions of users with positive emotions. The data collection unit can also identify data that is likely to be emotionally relatable. For example, it extracts data with a high degree of relatability based on the emotion analysis results. In this way, by performing data analysis based on the user's emotions, it is possible to identify data that is likely to be emotionally relatable.

[0076] The point assigning unit can estimate the user's emotional state in real time and assign points according to the emotional state. For example, the point assigning unit uses a generation AI to estimate the user's emotional state in real time and assign bonus points to users with positive emotions. For example, if a user shows an emotion of joy, additional points are assigned. The point assigning unit can also adjust the criteria for assigning points according to the user's emotional state. For example, if a user is feeling stressed, points for reducing stress are assigned. In this way, by assigning points according to the user's emotional state, it is possible to maintain the user's motivation.

[0077] The point assigning unit can analyze the user's answer history and assign points based on the consistency and quality of the answers. For example, the point assigning unit uses a generation AI to analyze the user's past answer history and assign bonus points to consistent answers. For example, additional points are assigned to users who have consistent opinions on the same topic. The point assigning unit can also adjust the point assignment criteria based on the quality of the user's answers. For example, bonus points are assigned to users who provide detailed answers. In this way, by assigning points based on the user's answer history, it is possible to maintain the user's motivation.

[0078] The point assigning unit can analyze a user's social media activity and assign points based on the activity. For example, the point assigning unit uses a generation AI to analyze a user's social media posts and assign points based on the activity. For example, the point assigning unit assigns bonus points to posts that receive many likes or shares. The point assigning unit can also adjust the criteria for assigning points based on the user's social media activity. For example, if a user shows interest in a particular topic, points are assigned for activities related to that topic. In this way, by assigning points based on the user's social media activity, it is possible to maintain the user's motivation.

[0079] The point assigning unit can assign bonus points to answers to specific topics based on the user's hobbies and interests. For example, the point assigning unit uses a generation AI to analyze the user's hobbies and interests and assigns bonus points to answers to specific topics based on the analysis. For example, a user who likes music can be assigned bonus points when answering a question about music. The point assigning unit can also adjust the criteria for assigning points to answers to specific topics in order to attract the user's interest. For example, bonus points can be assigned to topics in which the user has recently become interested. In this way, the user's motivation can be maintained by assigning points based on the user's hobbies and interests.

[0080] The point assigning unit can utilize the user's geographical information to assign bonus points to responses to region-specific questionnaires. For example, the point assigning unit uses a generation AI to analyze the user's geographical information and assign bonus points to responses to questionnaires specific to that region. For example, if the user lives in Tokyo, bonus points are assigned when the user answers a questionnaire about Tokyo. The point assigning unit can also adjust the point assignment criteria for questionnaires based on region-specific events or culture. For example, bonus points are assigned when the user answers a questionnaire about events held in a specific region. This allows the user's motivation to be maintained by assigning points based on the user's geographical information.

[0081] The point assigning unit uses the emotion estimation function to assign points at the timing when the user feels most motivated, thereby increasing the user's motivation to answer. The point assigning unit, for example, uses the emotion estimation function to identify the timing when the user feels most motivated and assigns points at that timing. For example, if the user shows an emotion of joy, additional points are assigned. The point assigning unit can also adjust the point assignment criteria based on the user's emotional state. For example, if the user is feeling stressed, points are assigned to reduce the stress. In this way, the user's motivation to answer can be increased by assigning points at the timing when the user feels most motivated.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The survey service can also analyze the user's health data and generate questions based on their health status. For example, it can analyze data from the user's fitness tracker to generate questions about their exercise habits. It can also generate questions about their sleep quality based on the user's sleep data. This allows the survey service to generate questions based on the user's health status and keep the user engaged.

[0084] The question generation unit can estimate the user's emotional state in real time and generate questions that correspond to that emotional state. For example, the generation AI analyzes the user's facial expressions and vocal tone to estimate the emotional state in real time. For example, if the user is feeling stressed, it generates questions that will help them relax. The question generation unit can also adjust the content and format of questions according to the user's emotional state. For example, if the user is expressing joy, it generates positive questions. This allows the system to generate questions that correspond to the user's emotional state and keep the user engaged.

[0085] The survey service can also analyze a user's purchasing history and generate questions based on their purchasing behavior. For example, it can generate questions to ask for feedback on a product the user recently purchased. It can also analyze a user's purchasing patterns and generate questions about the next product they might purchase. This allows the service to generate questions based on the user's purchasing behavior and keep the user engaged.

[0086] The question generation unit can analyze a user's past answer patterns and generate questions that take into account the consistency and changes in answers. For example, the generation AI analyzes a user's past answer data and generates questions that maintain the consistency of answers. For example, for a user who has consistently shown interest in questions about "travel" in the past, detailed questions about travel are generated. The question generation unit can also generate questions that take into account changes in the user's answer patterns. For example, if a user has recently started to show an interest in "sports," questions related to sports are generated. This makes it possible to continue to attract the user's interest by generating questions based on the user's past answer patterns.

[0087] The survey service can also analyze a user's learning history and generate questions based on the learning content. For example, if a user is taking an online course, the survey service can generate questions to check the user's understanding of the course. It can also generate questions about what the user should learn next based on the user's learning progress. This allows the survey service to keep the user engaged by generating questions based on the user's learning history.

[0088] The question generation unit can utilize the user's geographical information to generate questions specific to that region. For example, the generation AI analyzes the user's geographical information and generates questions specific to that region. For example, if the user lives in Tokyo, it can generate a question such as, "What are the recommended tourist spots in Tokyo?" The question generation unit can also generate questions based on local events and culture. For example, it generates questions about events held in a specific region. This allows the generation of questions based on the user's geographical information to continue to attract local interest.

[0089] The question generation unit uses the emotion estimation function to generate questions based on the topics that interest the user most, thereby maintaining the user's interest. For example, the emotion estimation function is used to identify the topics that interest the user most and generate questions based on those topics. For example, if the user shows a strong interest in "music," the question generation unit generates a question such as "What is your favorite song that you've listened to recently?" The question generation unit can also adjust the content and format of the question based on the user's emotional state. For example, if the user is relaxed, the question generation unit generates a detailed question. This allows the user's interest to be maintained by generating questions based on the topics that interest the user most.

[0090] The chat interface unit can estimate the user's emotional state in real time and generate a response that matches the emotional state. For example, the generation AI analyzes the user's facial expressions and voice tone to estimate the emotional state in real time. For example, if the user is tired, it will prioritize simple questions. The chat interface unit can also adjust the content and format of the response according to the user's emotional state. For example, if the user is expressing joy, it will generate a positive response. This allows the system to maintain the user's interest by generating a response that matches the user's emotional state.

[0091] The chat interface unit can analyze the user's response speed and adjust the difficulty of the question if the response is slow. For example, the generation AI can analyze the user's response speed in real time and lower the difficulty of the question if the response is slow. For example, it can change a complex question to a simpler one. The chat interface unit can also adjust the content and format of the question according to the user's response speed. For example, if the user is responding quickly, it can generate a more detailed question. This can reduce the user's stress by adjusting the difficulty of the question according to the user's response speed.

[0092] The chat interface unit can analyze the user's past response history and generate follow-up questions to maintain consistency in the responses. For example, the generation AI analyzes the user's past response history and generates follow-up questions to maintain consistency. For example, if a user has previously answered questions about "travel," the next question generated will be about "travel plans." The chat interface unit can also adjust the content and format of questions based on the user's response history. For example, if a user has consistently shown interest in a particular topic, follow-up questions related to that topic can be generated. This makes it possible to maintain the user's interest by generating follow-up questions based on the user's past response history.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The question generation unit learns the user's personal information and past survey responses and generates personalized questions. For example, if the user has previously answered a survey about "travel," the generation AI will generate a question such as "Where do you want to go on your next trip?" The generation AI can also generate questions based on the user's interests and concerns. Step 2: The chat interface unit presents the questions generated by the question generator to the user. For example, the user can easily participate in the survey at any time using a smartphone or PC. The chat interface unit is also designed so that the survey can proceed by the user simply entering simple answers such as "yes" or "no." Step 3: The data collection unit analyzes the user responses collected by the chat interface unit. For example, the generative AI analyzes the collected data and provides useful information for companies and researchers. The data collection unit can also compile survey results and provide reports on the popularity of specific products and consumer opinions. Step 4: The point allocating unit allocates points to the user based on the results of the analysis by the data collecting unit. For example, if a user answers 10 surveys, 100 points are allocated, and the user can use these points to obtain a gift card. The point allocating unit also allocates points for each survey answer to maintain the user's motivation.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0154] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0157] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question generation unit that learns the user's personal information and past questionnaire responses and generates personalized questions; a chat interface unit that presents the questions generated by the question generation unit to the user; a data collection unit that analyzes the user's responses collected by the chat interface unit; a point granting unit that grants points to a user based on the results of the analysis by the data collecting unit. A system characterized by:

2. The question generation unit Use sentiment estimation to generate questions based on the topics that interest users most, keeping them engaged 2. The system of claim 1.

3. The chat interface unit Estimating a user's emotional state in real time and generating a response according to said emotional state 2. The system of claim 1.

4. The data collection unit Estimate the user's emotional state in real time and perform data analysis based on the emotional state.

2. The system of claim 1.

5. The point giving unit The user's emotional state is estimated in real time, and points are awarded according to the emotional state.

2. The system of claim 1.

Images